Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	36.0	12.691177
daal4py_KNeighborsClassifier	0.0	5.0	27.063909
KNeighborsClassifier_kd_tree	0.0	2.0	42.927908
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	32.575493
KMeans_tall	0.0	0.0	24.670078
daal4py_KMeans_tall	0.0	0.0	9.769130
KMeans_short	0.0	0.0	3.673587
daal4py_KMeans_short	0.0	0.0	1.896952
LogisticRegression	0.0	0.0	25.947497
daal4py_LogisticRegression	0.0	0.0	4.999089
Ridge	0.0	0.0	11.359283
daal4py_Ridge	0.0	0.0	2.270106
HistGradientBoostingClassifier	0.0	5.0	3.405937
lightgbm	0.0	5.0	11.772575
xgboost	0.0	5.0	10.147203
catboost	0.0	5.0	2.831722
total	1.0	6.0	48.109077

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.316	0.000	2.535	0.000	1	100	NaN	NaN	0.490	0.000	0.643	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.194	0.199	0.000	0.025	1	100	0.936	0.713	3.895	0.044	6.469	0.089	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.196	0.003	0.000	0.196	1	100	1.000	1.000	0.104	0.002	1.886	0.044	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.125	0.000	6.422	0.000	-1	5	NaN	NaN	0.486	0.000	0.256	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	37.710	0.000	0.000	0.038	-1	5	0.820	0.812	3.846	0.042	9.804	0.108	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.195	0.010	0.000	0.195	-1	5	1.000	1.000	0.105	0.002	1.857	0.103	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.135	0.000	5.906	0.000	1	1	NaN	NaN	0.472	0.000	0.287	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	15.469	0.124	0.000	0.015	1	1	0.706	0.934	3.946	0.066	3.920	0.073	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.193	0.006	0.000	0.193	1	1	1.000	1.000	0.103	0.002	1.877	0.070	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.125	0.000	6.426	0.000	1	5	NaN	NaN	0.468	0.000	0.266	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	24.773	0.209	0.000	0.025	1	5	0.820	0.812	3.863	0.057	6.413	0.109	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.199	0.008	0.000	0.199	1	5	1.000	1.000	0.103	0.005	1.934	0.112	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.123	0.000	6.496	0.000	-1	100	NaN	NaN	0.459	0.000	0.268	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	38.197	0.000	0.000	0.038	-1	100	0.936	0.713	3.879	0.053	9.847	0.134	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.197	0.014	0.000	0.197	-1	100	1.000	1.000	0.105	0.002	1.882	0.137	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.121	0.000	6.586	0.000	-1	1	NaN	NaN	0.474	0.000	0.256	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	28.817	0.144	0.000	0.029	-1	1	0.706	0.934	3.990	0.051	7.223	0.100	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.183	0.011	0.000	0.183	-1	1	1.000	1.000	0.106	0.006	1.732	0.142	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.000	0.277	0.000	1	100	NaN	NaN	0.106	0.000	0.546	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.854	0.212	0.000	0.020	1	100	0.985	0.970	0.820	0.010	24.203	0.397	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.023	0.001	0.000	0.023	1	100	1.000	1.000	0.005	0.001	4.875	0.901	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.000	0.291	0.000	-1	5	NaN	NaN	0.106	0.000	0.517	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.773	0.000	0.000	0.033	-1	5	0.983	0.978	0.821	0.011	39.936	0.518	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.031	0.002	0.000	0.031	-1	5	1.000	1.000	0.004	0.000	7.038	0.557	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.282	0.000	1	1	NaN	NaN	0.107	0.000	0.529	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.852	0.065	0.000	0.011	1	1	0.979	0.981	0.878	0.009	12.359	0.145	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.016	0.001	0.000	0.016	1	1	1.000	1.000	0.004	0.000	3.716	0.194	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.000	0.289	0.000	1	5	NaN	NaN	0.117	0.000	0.474	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.707	0.247	0.000	0.020	1	5	0.983	0.978	0.821	0.014	24.010	0.517	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.023	0.001	0.000	0.023	1	5	1.000	1.000	0.004	0.000	5.599	0.473	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.305	0.000	-1	100	NaN	NaN	0.104	0.000	0.507	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	32.375	0.000	0.000	0.032	-1	100	0.985	0.970	0.817	0.009	39.620	0.423	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.030	0.002	0.000	0.030	-1	100	1.000	1.000	0.005	0.000	6.367	0.493	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.280	0.000	-1	1	NaN	NaN	0.110	0.000	0.521	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	23.788	0.132	0.000	0.024	-1	1	0.979	0.981	0.884	0.011	26.921	0.357	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.001	0.000	0.020	-1	1	1.000	1.000	0.004	0.000	4.548	0.373	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.903	0.000	0.028	0.000	1	5	NaN	NaN	0.760	0.000	3.818	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.501	0.027	0.000	0.002	1	5	0.977	0.978	0.682	0.016	2.202	0.064	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.001	0.000	2.460	0.922	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.835	0.000	0.028	0.000	-1	1	NaN	NaN	0.772	0.000	3.671	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.463	0.011	0.000	0.000	-1	1	0.965	0.966	0.128	0.004	3.604	0.131	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	1	1.000	1.000	0.000	0.000	15.288	7.164	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.920	0.000	0.027	0.000	1	1	NaN	NaN	0.738	0.000	3.960	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.801	0.020	0.000	0.001	1	1	0.965	0.982	0.229	0.007	3.501	0.136	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.435	1.982	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.931	0.000	0.027	0.000	1	100	NaN	NaN	0.781	0.000	3.756	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.954	0.084	0.000	0.005	1	100	0.979	0.978	0.679	0.013	7.297	0.185	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	1	100	1.000	1.000	0.001	0.000	4.641	2.181	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.753	0.000	0.029	0.000	-1	5	NaN	NaN	0.688	0.000	4.002	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.854	0.019	0.000	0.001	-1	5	0.977	0.982	0.224	0.006	3.818	0.127	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	5	1.000	1.000	0.000	0.000	15.125	6.904	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.815	0.000	0.028	0.000	-1	100	NaN	NaN	0.700	0.000	4.023	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.758	0.041	0.000	0.003	-1	100	0.979	0.966	0.124	0.002	22.242	0.535	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	-1	100	1.000	1.000	0.000	0.000	24.824	10.787	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.797	0.000	0.020	0.000	1	5	NaN	NaN	0.476	0.000	1.677	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.037	0.001	0.000	0.000	1	5	0.978	0.990	0.008	0.001	4.857	0.502	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.234	2.207	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.813	0.000	0.020	0.000	-1	1	NaN	NaN	0.499	0.000	1.630	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.037	0.003	0.000	0.000	-1	1	0.973	0.971	0.001	0.000	47.296	12.658	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	18.030	8.645	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.789	0.000	0.020	0.000	1	1	NaN	NaN	0.544	0.000	1.452	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.036	0.003	0.000	0.000	1	1	0.973	0.989	0.001	0.000	31.531	6.348	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.479	2.000	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.775	0.000	0.021	0.000	1	100	NaN	NaN	0.525	0.000	1.476	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.062	0.003	0.000	0.000	1	100	0.980	0.990	0.007	0.001	8.343	1.563	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.001	2.001	3.113	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.721	0.000	0.022	0.000	-1	5	NaN	NaN	0.484	0.000	1.490	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.038	0.003	0.000	0.000	-1	5	0.978	0.989	0.001	0.000	33.715	7.795	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	16.188	7.516	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.726	0.000	0.022	0.000	-1	100	NaN	NaN	0.489	0.000	1.484	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.056	0.005	0.000	0.000	-1	100	0.980	0.971	0.001	0.001	48.225	41.493	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	13.891	5.802	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.762	0.000	0.630	0.000	k-means++	NaN	30	NaN	0.324	0.0	2.348	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.001	0.251	0.000	k-means++	0.001	30	0.001	0.000	0.0	9.277	5.714	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	k-means++	1.000	30	1.000	0.000	0.0	8.931	3.876	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.553	0.000	0.867	0.000	random	NaN	30	NaN	0.277	0.0	1.996	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.311	0.000	random	0.001	30	0.000	0.000	0.0	7.012	2.465	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.000	0.000	0.001	random	1.000	30	1.000	0.000	0.0	9.039	3.791	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	7.479	0.000	3.209	0.000	k-means++	NaN	30	NaN	3.847	0.0	1.944	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	13.537	0.000	k-means++	0.002	30	0.002	0.000	0.0	6.210	1.952	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.015	0.002	k-means++	1.000	30	1.000	0.000	0.0	9.328	3.824	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.649	0.000	3.610	0.000	random	NaN	30	NaN	3.587	0.0	1.854	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	13.026	0.000	random	0.002	30	0.002	0.000	0.0	6.029	1.955	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.000	0.016	0.001	random	1.000	30	1.000	0.000	0.0	8.130	3.252	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.092	0.0	0.035	0.000	random	NaN	20	NaN	0.134	0.0	0.689	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.156	0.000	random	-0.001	20	-0.000	0.001	0.0	3.300	0.833	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	random	1.000	20	1.000	0.000	0.0	7.778	3.100	See	See
3	KMeans_short	fit	10000	10000	2	20	0.303	0.0	0.011	0.000	k-means++	NaN	20	NaN	0.047	0.0	6.451	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.148	0.000	k-means++	-0.001	20	-0.000	0.001	0.0	3.202	0.877	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	9.087	4.093	See	See
6	KMeans_short	fit	10000	10000	100	20	0.316	0.0	0.506	0.000	random	NaN	20	NaN	0.604	0.0	0.524	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.0	4.757	0.000	random	0.321	20	0.319	0.002	0.0	2.116	0.697	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.009	0.002	random	1.000	20	1.000	0.000	0.0	8.638	3.872	See	See
9	KMeans_short	fit	10000	10000	100	20	1.113	0.0	0.144	0.000	k-means++	NaN	20	NaN	0.250	0.0	4.462	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.0	5.183	0.000	k-means++	0.326	20	0.300	0.001	0.0	2.138	0.225	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.009	0.002	k-means++	1.000	20	1.000	0.000	0.0	8.061	2.882	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	15.930	0.0	[-0.07406812]	0.000	NaN	NaN	NaN	NaN	NaN	2.677	0.0	5.951	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[47.78599994]	0.000	NaN	NaN	NaN	NaN	0.554	0.000	0.0	0.911	0.281	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.18191824]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.341	0.222	See	See
3	LogisticRegression	fit	1000	1000	10000	[27]	1.242	0.0	[-1.71898079]	0.001	NaN	NaN	NaN	NaN	NaN	0.911	0.0	1.363	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[27]	0.002	0.0	[93.80609638]	0.000	NaN	NaN	NaN	NaN	0.250	0.004	0.0	0.592	0.077	See	See
5	LogisticRegression	predict	1000	1	10000	[27]	0.000	0.0	[17.71913691]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.158	0.069	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.281	0.000	0.284	0.0	NaN	NaN	NaN	0.279	0.000	1.008	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.011	0.001	7.134	0.0	NaN	NaN	0.119	0.021	0.005	0.543	0.131	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	0.613	0.0	NaN	NaN	NaN	0.000	0.000	0.862	0.627	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.440	0.000	0.555	0.0	NaN	NaN	NaN	0.345	0.000	4.177	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	4.428	0.0	NaN	NaN	1.000	0.000	0.000	0.755	0.380	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.009	0.0	NaN	NaN	NaN	0.000	0.000	0.658	0.531	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}